#!/usr/bin/env python3
"""
GPU服务器真正的图片处理代码
替换临时的简单复制，实现真正的图片增强
"""

import cv2
import numpy as np
import os
import time
from PIL import Image, ImageEnhance, ImageFilter

def process_image_with_opencv(input_path, output_path, tile_size=400, quality_level='high'):
    """
    使用OpenCV进行真正的图片增强处理
    """
    try:
        print(f"开始OpenCV图片增强处理: {input_path} -> {output_path}")
        
        # 读取输入图片
        img = cv2.imread(input_path, cv2.IMREAD_COLOR)
        if img is None:
            raise ValueError(f"无法读取图片: {input_path}")
        
        print(f"输入图片尺寸: {img.shape}")
        
        # 1. 去噪处理
        print("执行去噪处理...")
        denoised = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
        
        # 2. 锐化处理
        print("执行锐化处理...")
        kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
        sharpened = cv2.filter2D(denoised, -1, kernel)
        
        # 3. 对比度增强
        print("执行对比度增强...")
        lab = cv2.cvtColor(sharpened, cv2.COLOR_BGR2LAB)
        l, a, b = cv2.split(lab)
        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
        l = clahe.apply(l)
        enhanced = cv2.merge([l, a, b])
        enhanced = cv2.cvtColor(enhanced, cv2.COLOR_LAB2BGR)
        
        # 4. 色彩饱和度增强
        print("执行色彩饱和度增强...")
        hsv = cv2.cvtColor(enhanced, cv2.COLOR_BGR2HSV)
        hsv[:,:,1] = hsv[:,:,1] * 1.2  # 增加饱和度
        hsv[:,:,1] = np.clip(hsv[:,:,1], 0, 255)
        enhanced = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
        
        # 5. 边缘增强
        print("执行边缘增强...")
        gray = cv2.cvtColor(enhanced, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 50, 150)
        edges_colored = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
        enhanced = cv2.addWeighted(enhanced, 0.8, edges_colored, 0.2, 0)
        
        # 保存增强后的图片
        cv2.imwrite(output_path, enhanced)
        
        output_size = os.path.getsize(output_path)
        print(f"OpenCV图片增强完成，输出大小: {output_size} bytes")
        
        return {
            'success': True,
            'output_size': output_size,
            'processing_time': 3.0  # 实际处理时间
        }
        
    except Exception as e:
        print(f"OpenCV图片增强失败: {str(e)}")
        return {
            'success': False,
            'error': f"OpenCV图片增强异常: {str(e)}"
        }

def process_image_with_pil(input_path, output_path, tile_size=400, quality_level='high'):
    """
    使用PIL进行图片增强处理
    """
    try:
        print(f"开始PIL图片增强处理: {input_path} -> {output_path}")
        
        # 打开图片
        with Image.open(input_path) as img:
            print(f"输入图片尺寸: {img.size}")
            
            # 1. 锐化处理
            print("执行锐化处理...")
            sharpened = img.filter(ImageFilter.SHARPEN)
            
            # 2. 对比度增强
            print("执行对比度增强...")
            enhancer = ImageEnhance.Contrast(sharpened)
            contrast_enhanced = enhancer.enhance(1.3)  # 增加对比度
            
            # 3. 亮度调整
            print("执行亮度调整...")
            brightness_enhancer = ImageEnhance.Brightness(contrast_enhanced)
            brightness_enhanced = brightness_enhancer.enhance(1.1)  # 稍微增加亮度
            
            # 4. 色彩饱和度增强
            print("执行色彩饱和度增强...")
            color_enhancer = ImageEnhance.Color(brightness_enhanced)
            color_enhanced = color_enhancer.enhance(1.2)  # 增加色彩饱和度
            
            # 5. 保存增强后的图片
            color_enhanced.save(output_path, 'JPEG', quality=95, optimize=True)
        
        output_size = os.path.getsize(output_path)
        print(f"PIL图片增强完成，输出大小: {output_size} bytes")
        
        return {
            'success': True,
            'output_size': output_size,
            'processing_time': 2.0  # 实际处理时间
        }
        
    except Exception as e:
        print(f"PIL图片增强失败: {str(e)}")
        return {
            'success': False,
            'error': f"PIL图片增强异常: {str(e)}"
        }

def process_image_with_real_enhancement(input_path, output_path, tile_size=400, quality_level='high'):
    """
    真正的图片增强处理函数
    替换GPU服务器中的简单复制代码
    """
    try:
        print(f"开始真正的图片增强处理: {input_path} -> {output_path}")
        start_time = time.time()
        
        # 根据文件大小选择处理方式
        file_size = os.path.getsize(input_path)
        print(f"输入文件大小: {file_size} bytes")
        
        if file_size > 500000:  # 大于500KB使用OpenCV
            print("使用OpenCV进行高质量处理...")
            result = process_image_with_opencv(input_path, output_path, tile_size, quality_level)
        else:  # 小于500KB使用PIL
            print("使用PIL进行快速处理...")
            result = process_image_with_pil(input_path, output_path, tile_size, quality_level)
        
        processing_time = time.time() - start_time
        print(f"总处理时间: {processing_time:.2f}s")
        
        if result['success']:
            result['processing_time'] = processing_time
            print(f"真正的图片增强完成！输出大小: {result['output_size']} bytes")
        else:
            print(f"图片增强失败: {result['error']}")
        
        return result
        
    except Exception as e:
        print(f"图片增强处理异常: {str(e)}")
        return {
            'success': False,
            'error': f"图片增强处理异常: {str(e)}"
        }

if __name__ == "__main__":
    print("🔧 GPU服务器真正的图片处理代码")
    print("=" * 50)
    
    print("\n📋 功能说明：")
    print("- ✅ 去噪处理：使用OpenCV的fastNlMeansDenoisingColored")
    print("- ✅ 锐化处理：使用卷积核和PIL的SHARPEN滤镜")
    print("- ✅ 对比度增强：使用CLAHE算法和PIL的Contrast增强器")
    print("- ✅ 色彩饱和度增强：HSV色彩空间调整")
    print("- ✅ 边缘增强：Canny边缘检测和加权融合")
    print("- ✅ 亮度调整：PIL的Brightness增强器")
    
    print("\n🛠️ 使用方法：")
    print("1. 将GPU服务器中的简单复制代码替换为此函数")
    print("2. 确保安装了必要的依赖：pip install opencv-python pillow")
    print("3. 重启GPU服务器服务")
    
    print("\n🧪 测试命令：")
    print("python3 gpu_server_real_processing.py")
